The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventor, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present disclosure.
Precision navigation in obstacle-strewn environments such as the urban canyon or indoors is challenging. Typically GPS does not provide the precision required to navigate in obstacle rich environments. Also GPS reception requires line of sight access to at least four satellites, which is often not available in the urban canyon because of occlusion of the sky by buildings. Additionally, GPS radio signals are relatively weak, and can be jammed or spoofed (replaced by a false data stream that can mislead the targeted navigational system).
Higher-power radio navigation methods such as eLORAN may be less susceptible to jamming near the transmitter. Otherwise these methods inherit most of the vulnerabilities of GPS radio navigation, including susceptibility to denial of service by attack on the fixed transmitters. The precision of eLORAN localization is significantly less than GPS, and the global availability of eLORAN service has been significantly limited by the recent decision by the United Kingdom to discontinue eLORAN service in Europe.
3D map-matching is a navigational basis that is orthogonal to GPS and eLORAN navigation and consequently does not suffer from the same limitations and vulnerabilities. Early 2.5D map matching systems such as TERCOM (Terrain Contour Matching), were effectively employed in cruise missile navigation prior to GPS. The ability to pre-acquire detailed 3D environmental data has increased exponentially since the time of TERCOM. Moreover, commodity sensors are now available which generate real-time point-clouds that could potentially be matched to the pre-acquired 3D environmental data to provide rapid, precise localization in many GPS denied environments.
However, two problems have slowed the general adoption of efficient 3D map-match solutions. First, because the 3D environmental data sets are so large, it can be difficult to transmit and maintain them over existing networks using conventional data delivery approaches. Second, processing of these massive 3D datasets by 3D map-matching algorithms can be very inefficient because the matching algorithm is typically forced to process a large amount of occluded data that is irrelevant to the immediate 3D map-match localization solution. This is especially true in densely occluded natural terrains, indoors, or within the urban canyon, where buildings make most of the surfaces of the environment occluded from any small region.